AOPEDF

paper "Network-based Prediction of Drug-Target Interactions using an Arbitrary-Order Proximity Embedded Deep Forest"

'dataset' directory

• drug_dict.txt: list of drug unique identifier and drug names
• protein_dict.txt: list of protein unique identifier and protein names
• disease_dict.txt: list of disease unique identifier and disease names
• se_dict.txt: list of side effect unique identifier and side effect names
• drugdrug.txt: Drug-Drug interaction matrix
• drugDisease.txt: Drug-Disease association matrix
• drugsideEffect.txt: Drug-SideEffect association matrix
• drugsim1network.txt: Drug chemical similarity matrix
• drugsim2network.txt: Drug therapeutic similarity matrix
• drugsim3network.txt: Drug sequence similarity matrix
• drugsim4network.txt: Drug biological processes similarity matrix
• drugsim5network.txt: Drug cellular component similarity matrix
• drugsim6network.txt: Drug molecular function similarity matrix
• proteinprotein.txt: Protein-Protein interaction matrix
• proteinDisease.txt: Protein-Disease association matrix
• proteinsim1network.txt: Protein sequence similarity matrix
• proteinsim2network.txt: Protein biological processes similarity matrix
• proteinsim3network.txt: Protein cellular component similarity matrix
• proteinsim4network.txt: Protein molecular function similarity matrix
• Sim_drugDisease: Drug-Disease Jaccard similarity matrix
• Sim_drugsideEffect.txt: Drug-SideEffect Jaccard similarity matrix
• Sim_proteinDisease.txt: Protein-Disease Jaccard similarity matrix

Noted: Since drug-disease network, drug-side-effect network and protein-disease network are heterogeneous network, we calculate the corresponding similarity networks based on the Jaccard similarity coefficient for them

'AROPE' directory

This directory contains code necessary to use AROPE to extract arbitrary-Order proximity from different network

'gcforest' directory

This directory contains library of deep forest classifier

Basic Usage

$ python AOPEDF.py